class_docstr: Stochastic Gradient Riemannian Langevin Dynamics.
  
  This class implements the optimizer described in the paper *Stochastic Gradient
  Riemannian Langevin Dynamics on the Probability Simplex*, available at
  https://papers.nips.cc/paper/4883-stochastic-gradient-riemannian-langevin-dynamics-on-the-probability-simplex.pdf.
  
  Parameters
  ----------
  learning_rate : float, default 0.001
      The initial learning rate. If None, the optimization will use the
      learning rate from ``lr_scheduler``. If not None, it will overwrite
      the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``
      is also None, then it will be set to 0.01 by default.
  use_fused_step : bool, default False
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False, step is called,
      otherwise, fused_step is called.
docstr: Perform an optimization step using gradients and states.
  
  Parameters
  ----------
  indices : list of int
      List of unique indices of the parameters into the individual learning rates
      and weight decays. Learning rates and weight decay may be set via `set_lr_mult()`
      and `set_wd_mult()`, respectively.
  weights : list of NDArray
      List of parameters to be updated.
  grads : list of NDArray
      List of gradients of the objective with respect to this parameter.
  states : List of any obj
      List of state returned by `create_state()`.
docstr_tok: ["Perform","an","optimization","step","using","gradients","and","states",".","Parameters","--","--","--","--","--","indices",":","list","of","int","List","of","unique","indices","of","the","parameters","into","the","individual","learning","rates","and","weight","decays",".","Learning","rates","and","weight","decay","may","be","set","via","`","set_lr_mult","(",")","`","and","`","set_wd_mult","(",")","`",",","respectively",".","weights",":","list","of","NDArray","List","of","parameters","to","be","updated",".","grads",":","list","of","NDArray","List","of","gradients","of","the","objective","with","respect","to","this","parameter",".","states",":","List","of","any","obj","List","of","state","returned","by","`","create_state","(",")","`","."]
code: def step(self, indices, weights, grads, states):
      """Perform an optimization step using gradients and states.
  
      Parameters
      ----------
      indices : list of int
          List of unique indices of the parameters into the individual learning rates
          and weight decays. Learning rates and weight decay may be set via `set_lr_mult()`
          and `set_wd_mult()`, respectively.
      weights : list of NDArray
          List of parameters to be updated.
      grads : list of NDArray
          List of gradients of the objective with respect to this parameter.
      states : List of any obj
          List of state returned by `create_state()`.
      """
      for index, weight, grad in zip(indices, weights, grads):
          self._update_count(index)
          lr = self._get_lr(index)
          wd = self._get_wd(index)
  
          # preprocess grad
          grad *= self.rescale_grad
          if self.clip_gradient is not None:
              grad = clip(grad, -self.clip_gradient, self.clip_gradient)
          grad += wd * weight
  
          # update weight
          weight[:] -= lr / 2 * grad
          weight[:] += normal(
              0,
              math.sqrt(lr),
              shape=weight.shape,
              dtype=weight.dtype,
              ctx=weight.context,
          )
  
file_path: mxnet/python/mxnet/optimizer/sgld.py
imports: from __future__ import absolute_import
  import math
  from ..ndarray import clip
  from ..random import normal
  from .optimizer import Optimizer, register
label_desc: Use image node_id 3 for calling the SGLD obj's underlying member method code with example usage: obj.step(indices, weights, grads, states) without return types